End-to-End Machine Learning: logloss metric in R

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End-to-End Machine Learning: logloss metric in R

When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “log loss” or “cross-entropy loss”.

Log loss is a measure of the difference between the predicted probability of an event and its actual outcome. It is calculated by taking the natural logarithm of the ratio of the predicted probability and the actual outcome. A lower log loss value indicates a better model performance.

In R, there are several ways to calculate log loss, and several libraries such as caret, mlr, etc. which provide functions to calculate log loss. Some of the most popular functions are logLoss(), logloss() and cross_entropy() that can be used to calculate log loss.

Log loss is commonly used in classification problems especially when the outcome variable is binary or multi-class. It’s a good metric to evaluate the performance of models that predict probabilities such as logistic regression, decision trees, and neural networks.

It’s important to note that a log loss value closer to zero indicates a better model performance, and it’s important to compare the log loss value to other models to understand the performance of the model.

Overall, Log loss is a useful metric for evaluating the performance of a machine learning model in classification problems, especially when the outcome variable is binary or multi-class. It measures the difference between the predicted probability of an event and its actual outcome and a lower log loss value indicates a better model performance. It’s important to compare the log loss value to other models to understand the performance of the model.

 

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End-to-End Machine Learning: logloss metric in R

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